Bibliographic record
Abstract
The 2025 International Symposium on Energy Science and Engineering (ISESE 2025) was successfully held in Maoming, China, from May 16 to 18, 2025. Serving as an international platform for collaboration and exchange, the symposium aimed to foster open dialogue, knowledge sharing, and future partnerships among global experts in energy science and engineering. Attracting over 60 attendees, the conference culminated in rigorously peer-reviewed proceedings. These publications showcase high-quality interdisciplinary research, highlighting cutting-edge advances across the field. Additionally, the program featured a distinguished lineup of keynote speakers who elevated the academic discourse through their expertise: Prof. Marc A. Rosen (Ontario Tech University, Canada) examined hydrogen energy’s role in advancing sustainability and mitigating environmental impacts; Prof. Weiliang Wang (Jinan University, China) outlined theories and key technologies for state reconstruction of thermodynamic systems in next-generation power networks; Prof. Shunchun Yao (South China University of Technology, China) introduced an online coal property analysis method leveraging multispectral information fusion; Prof. Dou Bin (China University of Geosciences, China) explored AI and big data applications in geothermal energy development. List of Committee is available in this PDF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.522 | 0.360 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".